Overview

Dataset statistics

Number of variables35
Number of observations687
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory188.0 KiB
Average record size in memory280.2 B

Variable types

BOOL23
NUM10
CAT1
DATE1

Warnings

trading_global_discount_app has constant value "687" Constant
trading_global_event2 has constant value "687" Constant
Unnamed: 0.1 is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with Unnamed: 0.1High correlation
Unnamed: 0 has unique values Unique
Unnamed: 0.1 has unique values Unique
date has unique values Unique
GR_stringency_index has 379 (55.2%) zeros Zeros
delivery_express_days has 23 (3.3%) zeros Zeros
weekday_date has 98 (14.3%) zeros Zeros

Reproduction

Analysis started2021-03-25 17:25:33.400133
Analysis finished2021-03-25 17:25:47.441881
Duration14.04 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343
Minimum0
Maximum686
Zeros1
Zeros (%)0.1%
Memory size5.4 KiB
2021-03-25T17:25:47.557572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.3
Q1171.5
median343
Q3514.5
95-th percentile651.7
Maximum686
Range686
Interquartile range (IQR)343

Descriptive statistics

Standard deviation198.4641025
Coefficient of variation (CV)0.5786125439
Kurtosis-1.2
Mean343
Median Absolute Deviation (MAD)172
Skewness0
Sum235641
Variance39388
MonotocityStrictly increasing
2021-03-25T17:25:47.709735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
010.1%
 
46110.1%
 
45310.1%
 
45410.1%
 
45510.1%
 
45610.1%
 
45710.1%
 
45810.1%
 
45910.1%
 
46010.1%
 
Other values (677)67798.5%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
68610.1%
 
68510.1%
 
68410.1%
 
68310.1%
 
68210.1%
 

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343
Minimum0
Maximum686
Zeros1
Zeros (%)0.1%
Memory size5.4 KiB
2021-03-25T17:25:47.870864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.3
Q1171.5
median343
Q3514.5
95-th percentile651.7
Maximum686
Range686
Interquartile range (IQR)343

Descriptive statistics

Standard deviation198.4641025
Coefficient of variation (CV)0.5786125439
Kurtosis-1.2
Mean343
Median Absolute Deviation (MAD)172
Skewness0
Sum235641
Variance39388
MonotocityStrictly increasing
2021-03-25T17:25:48.025978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
010.1%
 
46110.1%
 
45310.1%
 
45410.1%
 
45510.1%
 
45610.1%
 
45710.1%
 
45810.1%
 
45910.1%
 
46010.1%
 
Other values (677)67798.5%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
68610.1%
 
68510.1%
 
68410.1%
 
68310.1%
 
68210.1%
 

date
Date

UNIQUE

Distinct687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2019-01-01 00:00:00
Maximum2020-11-17 00:00:00
2021-03-25T17:25:48.186199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:48.337318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

units_sold
Real number (ℝ≥0)

Distinct655
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9847.708879
Minimum4105
Maximum39019
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2021-03-25T17:25:48.503513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4105
5-th percentile6359
Q17830.5
median9071
Q310775
95-th percentile16438.7
Maximum39019
Range34914
Interquartile range (IQR)2944.5

Descriptive statistics

Standard deviation3596.058903
Coefficient of variation (CV)0.3651670604
Kurtosis14.38853721
Mean9847.708879
Median Absolute Deviation (MAD)1441
Skewness2.911538442
Sum6765376
Variance12931639.64
MonotocityNot monotonic
2021-03-25T17:25:48.658287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
842330.4%
 
894420.3%
 
723320.3%
 
732120.3%
 
1039720.3%
 
817020.3%
 
924820.3%
 
858220.3%
 
1062020.3%
 
948020.3%
 
Other values (645)66696.9%
 
ValueCountFrequency (%) 
410510.1%
 
415510.1%
 
420410.1%
 
421010.1%
 
425310.1%
 
ValueCountFrequency (%) 
3901910.1%
 
3578710.1%
 
3241710.1%
 
2750110.1%
 
2646410.1%
 

GR_stringency_index
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.04221252
Minimum0
Maximum76
Zeros379
Zeros (%)55.2%
Memory size5.4 KiB
2021-03-25T17:25:48.942631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q364
95-th percentile76
Maximum76
Range76
Interquartile range (IQR)64

Descriptive statistics

Standard deviation31.83498305
Coefficient of variation (CV)1.271252811
Kurtosis-1.571686612
Mean25.04221252
Median Absolute Deviation (MAD)0
Skewness0.593938682
Sum17204
Variance1013.466146
MonotocityNot monotonic
2021-03-25T17:25:49.050160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
037955.2%
 
62527.6%
 
76487.0%
 
64456.6%
 
10405.8%
 
68355.1%
 
72182.6%
 
66172.5%
 
60142.0%
 
6111.6%
 
Other values (10)284.1%
 
ValueCountFrequency (%) 
037955.2%
 
6111.6%
 
820.3%
 
10405.8%
 
1230.4%
 
ValueCountFrequency (%) 
76487.0%
 
7430.4%
 
72182.6%
 
7071.0%
 
68355.1%
 

weather_temperature
Real number (ℝ)

Distinct211
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.23580786
Minimum-0.4
Maximum28.2
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2021-03-25T17:25:49.173861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile4.53
Q18.1
median11.7
Q316.5
95-th percentile20.37
Maximum28.2
Range28.6
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation5.235199758
Coefficient of variation (CV)0.4278589381
Kurtosis-0.5126658685
Mean12.23580786
Median Absolute Deviation (MAD)4.1
Skewness0.2208301646
Sum8406
Variance27.40731651
MonotocityNot monotonic
2021-03-25T17:25:49.298752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.6101.5%
 
6.991.3%
 
9.291.3%
 
9.191.3%
 
7.481.2%
 
18.381.2%
 
6.881.2%
 
7.271.0%
 
8.271.0%
 
16.671.0%
 
Other values (201)60588.1%
 
ValueCountFrequency (%) 
-0.410.1%
 
0.810.1%
 
110.1%
 
1.110.1%
 
1.310.1%
 
ValueCountFrequency (%) 
28.210.1%
 
2710.1%
 
26.510.1%
 
25.910.1%
 
25.610.1%
 

marketing_spend
Real number (ℝ≥0)

Distinct675
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2385.117467
Minimum141
Maximum7244
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2021-03-25T17:25:49.425312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile309.06
Q12029
median2274.7
Q32742.05
95-th percentile4042.97
Maximum7244
Range7103
Interquartile range (IQR)713.05

Descriptive statistics

Standard deviation951.2724502
Coefficient of variation (CV)0.3988367295
Kurtosis3.337014264
Mean2385.117467
Median Absolute Deviation (MAD)326.4
Skewness0.6146360491
Sum1638575.7
Variance904919.2745
MonotocityNot monotonic
2021-03-25T17:25:49.548188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2080.720.3%
 
284.420.3%
 
2482.820.3%
 
2829.720.3%
 
2708.820.3%
 
2307.620.3%
 
2174.320.3%
 
2119.420.3%
 
2178.120.3%
 
292.320.3%
 
Other values (665)66797.1%
 
ValueCountFrequency (%) 
14110.1%
 
15110.1%
 
159.810.1%
 
16110.1%
 
17110.1%
 
ValueCountFrequency (%) 
724410.1%
 
6746.710.1%
 
6630.210.1%
 
6249.410.1%
 
6016.410.1%
 

delivery_standard_days
Real number (ℝ≥0)

Distinct41
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.188355167
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2021-03-25T17:25:49.662756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q35
95-th percentile8
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.068896003
Coefficient of variation (CV)0.4939638404
Kurtosis5.755584846
Mean4.188355167
Median Absolute Deviation (MAD)1
Skewness2.234697646
Sum2877.4
Variance4.280330673
MonotocityNot monotonic
2021-03-25T17:25:49.779623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
330344.1%
 
410715.6%
 
57410.8%
 
6568.2%
 
2426.1%
 
8152.2%
 
7142.0%
 
11111.6%
 
3.181.2%
 
1360.9%
 
Other values (31)517.4%
 
ValueCountFrequency (%) 
2426.1%
 
2.110.1%
 
2.310.1%
 
2.610.1%
 
2.710.1%
 
ValueCountFrequency (%) 
1420.3%
 
1360.9%
 
12.210.1%
 
1220.3%
 
11.810.1%
 

delivery_express_days
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.103202329
Minimum0
Maximum5.1
Zeros23
Zeros (%)3.3%
Memory size5.4 KiB
2021-03-25T17:25:49.888240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q11
median1
Q31.2
95-th percentile2.1
Maximum5.1
Range5.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.6464220373
Coefficient of variation (CV)0.5859505734
Kurtosis9.185607543
Mean1.103202329
Median Absolute Deviation (MAD)0.1
Skewness2.149683465
Sum757.9
Variance0.4178614503
MonotocityNot monotonic
2021-03-25T17:25:49.992656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
125837.6%
 
1.111516.7%
 
1.2426.1%
 
1.3416.0%
 
0.2416.0%
 
1.4233.3%
 
0233.3%
 
0.9223.2%
 
0.1152.2%
 
2131.9%
 
Other values (25)9413.7%
 
ValueCountFrequency (%) 
0233.3%
 
0.1152.2%
 
0.2416.0%
 
0.320.3%
 
0.460.9%
 
ValueCountFrequency (%) 
5.110.1%
 
520.3%
 
430.4%
 
3.910.1%
 
3.710.1%
 
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
473 
1
214 
ValueCountFrequency (%) 
047368.9%
 
121431.1%
 
2021-03-25T17:25:50.061680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
686 
1
 
1
ValueCountFrequency (%) 
068699.9%
 
110.1%
 
2021-03-25T17:25:50.094081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
1
390 
0
297 
ValueCountFrequency (%) 
139056.8%
 
029743.2%
 
2021-03-25T17:25:50.125479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
682 
1
 
5
ValueCountFrequency (%) 
068299.3%
 
150.7%
 
2021-03-25T17:25:50.158053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
672 
1
 
15
ValueCountFrequency (%) 
067297.8%
 
1152.2%
 
2021-03-25T17:25:50.190462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
642 
1
 
45
ValueCountFrequency (%) 
064293.4%
 
1456.6%
 
2021-03-25T17:25:50.221878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

trading_global_discount_app
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
687 
ValueCountFrequency (%) 
0687100.0%
 
2021-03-25T17:25:50.252280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
685 
1
 
2
ValueCountFrequency (%) 
068599.7%
 
120.3%
 
2021-03-25T17:25:50.279755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
678 
1
 
9
ValueCountFrequency (%) 
067898.7%
 
191.3%
 
2021-03-25T17:25:50.311158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

trading_global_event2
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
687 
ValueCountFrequency (%) 
0687100.0%
 
2021-03-25T17:25:50.342541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
650 
1
 
37
ValueCountFrequency (%) 
065094.6%
 
1375.4%
 
2021-03-25T17:25:50.369944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
622 
1
65 
ValueCountFrequency (%) 
062290.5%
 
1659.5%
 
2021-03-25T17:25:50.401396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
645 
1
 
42
ValueCountFrequency (%) 
064593.9%
 
1426.1%
 
2021-03-25T17:25:50.432804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
673 
1
 
14
ValueCountFrequency (%) 
067398.0%
 
1142.0%
 
2021-03-25T17:25:50.465209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
660 
1
 
27
ValueCountFrequency (%) 
066096.1%
 
1273.9%
 
2021-03-25T17:25:50.496660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
641 
1
 
46
ValueCountFrequency (%) 
064193.3%
 
1466.7%
 
2021-03-25T17:25:50.528174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
669 
1
 
18
ValueCountFrequency (%) 
066997.4%
 
1182.6%
 
2021-03-25T17:25:50.558592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
662 
1
 
25
ValueCountFrequency (%) 
066296.4%
 
1253.6%
 
2021-03-25T17:25:50.590025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
685 
1
 
2
ValueCountFrequency (%) 
068599.7%
 
120.3%
 
2021-03-25T17:25:50.621502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
686 
1
 
1
ValueCountFrequency (%) 
068699.9%
 
110.1%
 
2021-03-25T17:25:50.652893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
397 
1
290 
ValueCountFrequency (%) 
039757.8%
 
129042.2%
 
2021-03-25T17:25:50.685349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
615 
1
72 
ValueCountFrequency (%) 
061589.5%
 
17210.5%
 
2021-03-25T17:25:50.716755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
362 
1
325 
ValueCountFrequency (%) 
036252.7%
 
132547.3%
 
2021-03-25T17:25:50.749379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

year_date
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2019
365 
2020
322 
ValueCountFrequency (%) 
201936553.1%
 
202032246.9%
 
2021-03-25T17:25:50.808204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-25T17:25:50.861905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:50.922821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

month_date
Real number (ℝ≥0)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.187772926
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size5.4 KiB
2021-03-25T17:25:51.000979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.29120599
Coefficient of variation (CV)0.531888618
Kurtosis-1.152459655
Mean6.187772926
Median Absolute Deviation (MAD)3
Skewness0.03742277812
Sum4251
Variance10.83203687
MonotocityNot monotonic
2021-03-25T17:25:51.080063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
1629.0%
 
3629.0%
 
5629.0%
 
7629.0%
 
8629.0%
 
10629.0%
 
4608.7%
 
6608.7%
 
9608.7%
 
2578.3%
 
Other values (2)7811.4%
 
ValueCountFrequency (%) 
1629.0%
 
2578.3%
 
3629.0%
 
4608.7%
 
5629.0%
 
ValueCountFrequency (%) 
12314.5%
 
11476.8%
 
10629.0%
 
9608.7%
 
8629.0%
 

weekday_date
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997088792
Minimum0
Maximum6
Zeros98
Zeros (%)14.3%
Memory size5.4 KiB
2021-03-25T17:25:51.158221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.001455075
Coefficient of variation (CV)0.6677997263
Kurtosis-1.251448651
Mean2.997088792
Median Absolute Deviation (MAD)2
Skewness0.002917585377
Sum2059
Variance4.005822416
MonotocityNot monotonic
2021-03-25T17:25:51.231246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
19914.4%
 
09814.3%
 
29814.3%
 
39814.3%
 
49814.3%
 
59814.3%
 
69814.3%
 
ValueCountFrequency (%) 
09814.3%
 
19914.4%
 
29814.3%
 
39814.3%
 
49814.3%
 
ValueCountFrequency (%) 
69814.3%
 
59814.3%
 
49814.3%
 
39814.3%
 
29814.3%
 

Interactions

2021-03-25T17:25:34.787803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:34.897012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.000410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.105659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.207982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.309222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.422913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.532629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.636722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.744149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.850526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:35.957064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.064499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.173858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.283412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.385711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.494927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.596085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.698194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.798792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:36.904052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.017849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.125635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.233018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.337448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.437947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.561855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.682572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.800306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:37.916866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.041542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.170198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.292663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.413406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.534132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.649854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.771628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:38.896296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.013404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.427142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.536062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.641379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.745536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.853923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:39.960134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.061486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.169462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.272640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.372891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.471075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.574655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.687122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.800812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:40.916340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.031237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.138330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.251400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.363006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.472000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.580606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.689197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.796072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:41.902467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.012455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.120005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.223502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.331212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.436726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.539288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.641889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.745342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.847819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:42.952322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.057849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.162386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.260704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.366390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.573852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.670468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.766737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.867135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:43.968629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.072141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.176501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.281078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.381579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.488035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.590602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.689164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.785347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.887830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:44.995280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.101774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.209379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.313883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.415233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.523087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.631991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.744226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:45.856206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-25T17:25:51.361108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-25T17:25:51.771596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-25T17:25:52.175396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-25T17:25:52.731581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-25T17:25:46.156006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-25T17:25:47.223607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Unnamed: 0Unnamed: 0.1dateunits_soldGR_stringency_indexweather_temperaturemarketing_spenddelivery_standard_daysdelivery_express_daystrading_global_sale1trading_global_sale2trading_global_promotiontrading_global_flashsaletrading_global_spendnsavetrading_global_discount_extratrading_global_discount_apptrading_global_discount_studenttrading_global_event1trading_global_event2trading_local_sale1trading_local_sale2trading_local_promotiontrading_local_flashsaletrading_local_spendnsavetrading_local_discount_extratrading_local_discount_apptrading_local_discount_studenttrading_local_event1trading_local_event2weather_cloudyweather_rainyweather_clearyear_datemonth_dateweekday_date
0002019-01-017408.00.07.22367.44.01.110000000001000000000100201911
1112019-01-027163.00.09.12377.94.01.110000000001000000000100201912
2222019-01-038043.00.06.82388.44.01.110000000001000000000001201913
3332019-01-047395.00.03.82398.94.01.110000000001000000000100201914
4442019-01-057294.00.02.92409.44.01.110000000001000000000001201915
5552019-01-066249.00.07.02419.14.01.110000000001000000000100201916
6662019-01-076369.00.08.62428.94.01.110000000001000000000100201910
7772019-01-087431.00.010.32438.74.01.110000000001000000000100201911
8882019-01-098072.00.07.62448.54.01.110000000000000000100001201912
9992019-01-108117.00.06.62458.34.01.110000000000000000100100201913

Last rows

Unnamed: 0Unnamed: 0.1dateunits_soldGR_stringency_indexweather_temperaturemarketing_spenddelivery_standard_daysdelivery_express_daystrading_global_sale1trading_global_sale2trading_global_promotiontrading_global_flashsaletrading_global_spendnsavetrading_global_discount_extratrading_global_discount_apptrading_global_discount_studenttrading_global_event1trading_global_event2trading_local_sale1trading_local_sale2trading_local_promotiontrading_local_flashsaletrading_local_spendnsavetrading_local_discount_extratrading_local_discount_apptrading_local_discount_studenttrading_local_event1trading_local_event2weather_cloudyweather_rainyweather_clearyear_datemonth_dateweekday_date
6776776772020-11-088657.060.010.74598.43.00.9001000000000000000001002020116
6786786782020-11-099209.060.012.44479.43.00.2001000000000000000000102020110
6796796792020-11-1010182.060.010.74360.43.01.1001000000000000000001002020111
6806806802020-11-1115157.060.010.54241.53.01.1001000000000000001001002020112
6816816812020-11-1213348.060.012.94122.53.01.2001000000000000001001002020113
6826826822020-11-1314180.060.012.14003.54.01.4001000000000000001001002020114
6836836832020-11-1410397.060.07.83884.54.01.6001000000000000000001002020115
6846846842020-11-1513498.060.06.34274.13.00.7001001000000000000001002020116
6856856852020-11-1612230.060.011.04663.73.00.2001001000000000000001002020110
6866866862020-11-1712676.060.09.35053.33.01.6001001000000000000001002020111